SoroushVahidi/scidocs huggingface dataset:
Fast Pattern Matching in Strings
(1977)

テキストの中から与えられたパターンを見つけ出すと いう,いわゆる文字列探索のアルゴリズムを調べようと アルゴリズムの教科書をひもとくと,必ずといってよい ほど,力まかせの方法,Knuth,Morris,Prattによる 方法,BoyerとMooreによる方法 の 3つが紹介され ている.本論文は 2番目の方法-著者の頭文字をとっ て KMPアルゴリズムと省略される について述べて いる. 文字列探索が必要とされる代表的な例に,テキストエ ディタの探索コマンドがある.実際のテキストエディタ の探索コマンドでは,探索する文字列がきわめて短い場 合には力まかせの方法が用いられることがある以外は, 第 3の方法これも著者の頭文字から BMアルゴリズ ムと省略されるが利用される場合が多いようではあ るが,KMPアルゴリズムは BMアルゴリズムと並び,ア イディア 1つで計算量が改善されるというアルゴリズム の面白さを,我々に十分教えてくれる. 本論文は,KMPアルゴリズムの動作を例を用いて解 説することから始まり,具体的なアルゴリズムの提示, アルゴリズムの改良と拡張,計算量に関する理論的な考 察と続く.アルゴリズムを提案する論文としてはここま でで十分なのだが,本論文はさらに,長さが偶数の回文 文字列の話,KMPアルゴリズム発明の歴史,ついには 後書きと称して BMアルゴリズムに関する考察にまで話 が及ぶという,特異とも思える構成となっている. ここで述べられている歴史によると,KMPアルゴリズ ムは,元々はMorris,および,Knuth,Prattチームによ り独立に発明された.Morrisは,1969年に CDC 6400と いう計算機上のテキストエディタを作成していた際,有 限オートマトン理論を利用して,このアルゴリズムの原 形を考えた.またこれとは別に,Knuthは 1970年に, 長さが偶数の回文文字列の 2方向プッシュダウンオート マトンによる認識の理論の調査をしているうちに,その 背後の文字列探索アルゴリズムの存在に気づき,本アル ゴリズムの発明に至ったという.これは,Knuthにとっ て,オートマトン理論が現実のプログラミングの問題の 解決に役に立った初めての経験であったらしい.Robert Sedgewickは,その有名なアルゴリズムの教科書 の文 字列探索の章の中で,このことを「理論的な結果が思い がけず直接実用と結びついた稀な実例である」と指摘し ているが,実際この経験は,Knuthにとってきわめて印 象深いものであったに違いない.そう考えると,上で述 べたような本論文の特異とも思える構成も,著者による 思いの発露と思えて興味深い.ちなみに,本論文が掲載 されている SIAM Journal on Computingの同じ号には, Sedgewickの論文も掲載されている. Morrisがこのアルゴリズムを思いついた背景には, CDC 6400でのバッファリング処理が複雑であるため, テキストの「後戻り」を必要としない方法が求められて いたことがあった.最近のように主記憶をふんだんに使 うことができなかったという不自由さが,新しいアルゴ リズムの発明に結びついたといえよう. 本論文の KMPアルゴリズムの改良の章には,番兵を 用いた高速化の話も述べられている.力ずく的に記述さ れたプログラムが多い現在では,番兵は以前ほどは使わ れなくなってしまったが,本論文が発表された 1970年代 後半では,番兵は美しく高速なプログラムを記述するた めの重宝な技術であった.このようなところにも,時代 の流れを感じさせるものがある. KMPアルゴリズムは,素朴なアルゴリズムから賢いア ルゴリズムをプログラム変換によって導出する例題とし てもよく用いられる.このような題材としても KMPア ルゴリズムが役に立つことになろうとは,著者たちは想 定していなかったであろう.
Finite Automata and Their Decision Problems
(1959)

Finite automata are considered in this paper a s instruments for classifying finite tapes. Each onetape automaton defines a set of tapes, a two-tape automaton defines a set of pairs of tapes, et cetera. The structure of the defined sets is studied. Various generalizations of the notion of an automaton are introduced and their relation to the classical automata is determined. Some decision problems concerning automata are shown to be solvable by effective algorithms; others turn out to be unsolvable by algorithms.
Improving Video Game Development: Facilitating Heterogeneous Team Collaboration through Flexible Software Processes
(2010)

Based on our observations in the Austrian video game software development practices we identified a lack of systematic processes and method support and inefficient collaboration between various involved disciplines, i.e. engineers and artists. Video game development includes heterogeneous disciplines, e.g. creative arts, game and content design, and software. Nevertheless, improvement of team collaboration and process support is an ongoing challenge to enable a comprehensive view on game development projects. Lessons learned from software engineering practices can help game developers to increase game development processes within a heterogeneous environment. Based on a state of the practice survey in the Austrian games industry, this paper presents (a) first results with focus on process and method support and (b) suggests a candidate flexible process approach based on Scrum to improve video game development and team collaboration. The results showed (a) a trend to highly flexible software processes involving various disciplines and (b) identified the suggested flexible process approach as feasible and useful for project application.
Compression of multiple depth maps for IBR
(2005)

Image-based rendering techniques include those with geometry and those without. Geometric information in the form of a depth map aligned with the image holds a lot of promise for IBR due to the several methods available to capture it. It can improve the quality of generated views using a limited number of views. Compression of light fields or multiple images has attracted a lot of attention in the past. Compression of multiple depth maps of the same scene has not been explored much in the literature. We propose a method for compressing multiple depth maps in this paper using geometric proxy. Different quality of rendering and compression ratio can be achieved by varying different parameters. Experiments show the effectiveness of the compression technique on several model data.
Smart contract-based campus demonstration of decentralized transactive energy auctions
(2017)

Transactive energy paradigms will enable the exchange of energy from a distributed set of prosumers. While prosumers have access to distributed energy resources, these resources are intermittently available. There is a need for distributed markets to enable the exchange of energy in transactive environments, however, the large number of potential prosumers introduces challenges in the establishment of trust between prosumers. Markets for transactive environments create other challenges, such as establishing clearing prices for energy and exchanging money between prosumers. Blockchains provide a unique technology to address this distributed trust problem through the use of a distributed ledger, cryptocurrencies, and the execution of smart contracts. This paper introduces a smart contract that implements a transactive energy auction that operates without the need for a trusted entitys oversight. The auction mechanism implements a Vickrey second price auction, which guarantees bidders will submit honest bids. The contract is implemented on transactive agents on the WSU campus interacting with a 72kW PV array and the Ethereum blockchain. The contract is then used to execute auctions based on the energy from the the PV array and simulated building loads to demonstrate the auctions operations.
RAMSYS: Resource-Aware Asynchronous Data Transfer with Multicore SYStems
(2017)

High-speed data transfer is vital to data-intensive computing that often requires moving large data volumes efficiently within a local data center and among geographically dispersed facilities. Effective utilization of the abundant resources in modern multicore environments for data transfer remains a persistent challenge, particularly, for Non-Uniform Memory Access (NUMA) systems wherein the locality of data accessing is an important factor. This requires rethinking how to exploit parallel access to data and to optimize the storage and network I/Os. We address this challenge and present a novel design of asynchronous processing and resource-aware task scheduling in the context of high-throughput data replication. Our software allocates multiple sets of threads to different stages of the processing pipeline, including storage I/O and network communication, based on their capacities. Threads belonging to each stage follow an asynchronous model, and attain high performance via multiple locality-aware and peer-aware mechanisms, such as task grouping, buffer sharing, affinity control and communication protocols. Our design also integrates high performance features to enhance the scalability of data transfer in several scenarios, e.g., file-level sorting, block-level asynchrony, and thread-level pipelining. Our experiments confirm the advantages of our software under different types of workloads and dynamic environments with contention for shared resources, including a 28-160 percent increase in bandwidth for transferring large files, 1.7-66 times speed-up for small files, and up to 108 percent larger throughput for mixed workloads compared with three state of the art alternatives, GridFTP , BBCP and Aspera.
A Review of the Status of Brain Structure Research in Transsexualism
(2016)

The present review focuses on the brain structure of male-to-female (MtF) and female-to-male (FtM) homosexual transsexuals before and after cross-sex hormone treatment as shown by in vivo neuroimaging techniques. Cortical thickness and diffusion tensor imaging studies suggest that the brain of MtFs presents complex mixtures of masculine, feminine, and demasculinized regions, while FtMs show feminine, masculine, and defeminized regions. Consequently, the specific brain phenotypes proposed for MtFs and FtMs differ from those of both heterosexual males and females. These phenotypes have theoretical implications for brain intersexuality, asymmetry, and body perception in transsexuals as well as for Blanchard's hypothesis on sexual orientation in homosexual MtFs. Falling within the aegis of the neurohormonal theory of sex differences, we hypothesize that cortical differences between homosexual MtFs and FtMs and male and female controls are due to differently timed cortical thinning in different regions for each group. Cross-sex hormone studies have reported marked effects of the treatment on MtF and FtM brains. Their results are used to discuss the early postmortem histological studies of the MtF brain.
Pregnancy-related low back pain.
(2011)

Pregnancy related low back pain is a common complaint among pregnant women. It can potentially have a negative impact on their quality of life. The aim of this article is to present a current review of the literature concerning this issue.By using PubMed database and low back pain, pelvic girdle pain, pregnancy as keywords, abstracts and original articles in English investigating the diagnosis treatment of back pain during pregnancy were searched and analyzedLow back pain could present as either a pelvic girdle pain between the posterior iliac crest and the gluteal fold or as a lumbar pain over and around the lumbar spine. The source of the pain should be diagnosed and differentiated early.The appropriate treatment aims to reduce the discomfort and the impact on the pregnant womans quality of life. This article reveals the most common risk factors, as well as treatment methods, which may help to alleviate the pain. Some suggestions for additional research are also discussed.
Refining the relationship between personality and subjective well-being.
(2008)

Understanding subjective well-being (SWB) has historically been a core human endeavor and presently spans fields from management to mental health. Previous meta-analyses have indicated that personality traits are one of the best predictors. Still, these past results indicate only a moderate relationship, weaker than suggested by several lines of reasoning. This may be because of commensurability, where researchers have grouped together substantively disparate measures in their analyses. In this article, the authors review and address this problem directly, focusing on individual measures of personality (e.g., the Neuroticism-Extroversion-Openness Personality Inventory; P. T. Costa & R. R. McCrae, 1992) and categories of SWB (e.g., life satisfaction). In addition, the authors take a multivariate approach, assessing how much variance personality traits account for individually as well as together. Results indicate that different personality and SWB scales can be substantively different and that the relationship between the two is typically much larger (e.g., 4 times) than previous meta-analyses have indicated. Total SWB variance accounted for by personality can reach as high as 39% or 63% disattenuated. These results also speak to meta-analyses in general and the need to account for scale differences once a sufficient research base has been generated.
Scene classification based on single-layer SAE and SVM
(2015)

Scene classification aims to group images into semantic categories. It is a challenging problem in computer vision due to the difficulties of intra-class variability and inter-class similarity. In this paper, a scene classification approach based on single-layer sparse autoencoder (SAE) and support vector machine (SVM) is proposed. This approach consists of two steps: SAE-based feature learning step and SVM-based classification step. In the first step, a single-layer SAE network is constructed and trained by the patches which are sampled randomly from the source images. The feature representation of images is learned by the trained single-layer SAE network. Meanwhile, a pooling operation is used to reduce the dimension of the learned feature vectors. In the second step, in order to improve the classification accuracy, the parameters of SVM are optimized by a particle swarm optimization (PSO) based algorithm. The one-versus-one strategy is employed for the multiple scene classification problem. To show the efficiency of the proposed approach, several public data sets are employed. The results reveal that the proposed approach achieves better classification accuracy than the existing state-of-the-art methods. 2014 Elsevier Ltd. All rights reserved.
An Ultra-Wideband Horizontally Polarized Omnidirectional Circular Connected Vivaldi Antenna Array
(2017)

An ultra-wideband horizontally polarized omnidirectional antenna is proposed. The connected Vivaldi antenna array mechanism is exploited to achieve the ultra-wide bandwidths of both the impedance and the radiation patterns simultaneously. Sixteen Vivaldi antenna elements are connected with each other and distributed along a circular substrate to form the proposed antenna. The designed antenna exhibits a relative impedance bandwidth of 159.97% (1.28–11.51 GHz) for <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert <-10$ </tex-math></inline-formula> dB. Meanwhile, uniform omnidirectional radiation patterns with a gain variation of less than 1.5 dB have been obtained within the frequency band of 1.25–7.6 GHz (142.73%).
A Social-Network-Based Cryptocurrency Wallet-Management Scheme
(2018)

Effective cryptocurrency key management has become an urgent requirement for modern cryptocurrency. Although a large body of cryptocurrency wallet-management schemes has been proposed, they are mostly constructed for specific application scenarios and often suffer from weak security. In this paper, we propose a more effective, usable, and secure cryptocurrency wallet-management system based on semi-trusted social networks, therein allowing users to collaborate with involved parties to achieve some powerful functions and recovery under certain circumstances. Furthermore, we employ an identity-based hierarchical key-insulated encryption scheme to achieve time-sharing authorization and present a semi-trusted portable social-network-based wallet-management scheme that provides the features of security-enhanced storage, portable login on different devices, no-password authentication, flexible key delegation, and so on. The performance analysis shows that our proposed schemes require minimal additional overhead and have low time delays, making them sufficiently efficient for real-world deployment.
A Framework for Analysis of Road Accidents
(2018)

The road accident data analysis use data mining and machine learning techniques, focusing on identifying factors that affect the severity of an accident. There are a variety of reasons that contribute to accidents. Some of them are internal to the driver but many are external. For example, adverse weather conditions like fog, rainfall or snowfall cause partial visibility and it may become difficult as well as risky to drive on such roads. It is expected that the findings from this paper would help civic authorities to take proactive actions on likely crash prone weather and traffic conditions.
Low delay MPEG DASH streaming over the WebRTC data channel
(2016)

Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is becoming the de-facto data format and streaming solution for over-the-top (OTT) video on-demand streaming. The underlying HTTP transport has its limitations and known penalties in slow start, under-utilization, and other inefficiency. An alternate approach to DASH is the new transport schemes like HTTP/2.0 and WebSocket. In this work, we explore WebRTC as a streaming transport to carry DASH data and demonstrate that it is competitive in serving low delay streaming applications with fast channel switching, with proper video data coding and streaming signaling/control solutions.
Single Image 3D Interpreter Network
(2016)

Understanding 3D object structure from a single image is an important but difficult task in computer vision, mostly due to the lack of 3D object annotations in real images. Previous work tackles this problem by either solving an optimization task given 2D keypoint positions, or training on synthetic data with ground truth 3D information. In this work, we propose 3D INterpreter Network (3D-INN), an endto-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, trained on both real 2D-annotated images and synthetic 3D data. This is made possible mainly by two technical innovations. First, we propose a Projection Layer, which projects estimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D structural parameters supervised by 2D annotations on real images. Second, heatmaps of keypoints serve as an intermediate representation connecting real and synthetic data, enabling 3D-INN to benefit from the variation and abundance of synthetic 3D objects, without suffering from the difference between the statistics of real and synthesized images due to imperfect rendering. The network achieves state-of-the-art performance on both 2D keypoint estimation and 3D structure recovery. We also show that the recovered 3D information can be used in other vision applications, such as 3D rendering and image retrieval.
Real time face recognition engine using compact features for electronics key
(2016)

In this research, a design and implementation of real-time face recognition engine using compact features vector extracted by linear binary pattern (LBP) and zoned discrete cosine transforms (DCT) analysis is proposed for electronics key. The function of LBP is to normalize the lighting variations of the input face image and the function of zoned DCT is to define the local descriptors of the face image. In order to get more compact features vector, the predictive LDA is employed for dimensional reduction. The aims of this research is to develop fast and strong face recognition that can be implemented for electronic key which will be implemented for substituting current security system (PIN and password). In addition, the recognition engine is also designed for real time face recognition which can work on hardware having limited resources such as Intel atom computer, raspberry pi, and android smart phone. The experimental results show that the proposed engine provide high enough recognition rate, and small false rejection rate (FRR) and false acceptance rate (FAR). In addition, the engine needs short processing time.
Web page revisitation revisited: implications of a long-term click-stream study of browser usage
(2007)

This paper presents results of an extensive long-term click-stream study of Web browser usage. Focusing on character and challenges of page revisitation, previous findings from seven to thirteen years ago are updated. The term page re-visit had to be differentiated, since the recurrence rate--the key measure for the share of page revisits--turns out to strongly depend on interpretation. We identify different types of revisitation that allow assessing the quality of current user support and developing concepts for new tools.
 Individual navigation strategies differ dramatically and are strongly influenced by personal habits and type of site visited. Based on user action logs and interviews, we distinguished short-term revisits (backtrack or undo) from medium-term (re-utilize or observe) and long-term revisits (rediscover). We analyze current problems and provide suggestions for improving support for different revisitation types.
Streaming End-to-end Speech Recognition For Mobile Devices
(2018)

End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.
Human facial illustrations: Creation and psychophysical evaluation
(2004)

We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research.
Information-centric sensor networks for cognitive IoT: an overview
(2017)

Information-centric sensor networks (ICSNs) are a paradigm of wireless sensor networks that focus on delivering information from the network based on user requirements, rather than serving as a point-to-point data communication network. Introducing learning in such networks can help to dynamically identify good data delivery paths by correlating past actions and results, make intelligent adaptations to improve the network lifetime, and also improve the quality of information delivered by the network to the user. However, there are several factors and limitations that must be considered while choosing a learning strategy. In this paper, we identify some of these factors and explore various learning techniques that have been applied to sensor networks and other applications with similar requirements in the past. We provide our recommendation on the learning strategy based on how well it complements the needs of ICSNs, while keeping in mind the cost, computation, and operational overhead limitations.
3-D Path Planning and Target Trajectory Prediction for the Oxford Aerial Tracking System
(2007)

For the Oxford Aerial Tracking System (OATS) we are developing a robot helicopter that can track moving ground objects. Here we describe algorithms for the device to perform path planning and trajectory prediction. The path planner uses superquadratic potential fields and incorporates a height change mechanism that is triggered where necessary and in order to avoid local minima traps. The goal of the trajectory prediction system is to reliably predict the target trajectory during occlusion caused by ground obstacles. For this we use two artificial neural networks in parallel whose retraining is automatically triggered if major changes in the target behaviour pattern are detected. Simulation results for both systems are presented.